Research map / Learning theory and algorithms / Combinatorial and search optimization
Escaping Local Optima with Non-Elitist Evolutionary Algorithms
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use non-elitist 3-tournament selection with mutation rate close to but below the error threshold | population-based EA · FUNNEL | expected runtime: O(nλ log(λ)+n2 log(n)) | Helped |
| replace elitism with non-elitist 3-tournament selection | population-based EA · FUNNEL | runtime: Pr(T ≤ ecnd) ≤ e−Ω(nd) | Helped |
| use tournament selection instead of elitist selection | Set Cover EA · CYC.7 | average cover size | Helped |
| set the mutation rate close to but below the error threshold | 3-tournament EA · Set Cover, CYC.7 | average cover size: χ = 1.08 | Helped |
| replace 3-tournament selection with (µ, λ)-selection (ablation) | non-elitist EA · FUNNEL | runtime: Pr (T ≤ ecn) ≤ e−Ω(n) | Hurt |
| use mutation rate χ ≤ ln(ξ(1 − δ)) (ablation) | 3-tournament EA · FUNNEL | runtime: Pr (T ≤ ecn) ≤ e−Ω(λ) | Hurt |
| deviate too much from the error-threshold mutation rate (ablation) | non-elitist EA · FUNNEL | runtime: Pr (T ≤ ecn) ≤ e−Ω(n) | Hurt |
| use a mutation rate slightly below the error threshold | tournament EAs · Set Cover, Stein.81 | success frequency | Mixed |
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